/*
* this test is for finding the best optimization for SVM algorithm
* trying sigma and gamma for the rbf kernel for best accuracy.
*/
private void Internal_SVM(ImageVector[] goodImages, ImageVector[] badImages)
{
for (double i = 0; i <= 5; i += 1)
{
for (double j = 0; j <= 0; j += 1)
{
double sigma = Math.Pow(2, i);
double gamma = Math.Pow(2, j);
//learningAlgo = new SVM(userPath, i, 1, SVM.Kernel.SVM_POLY, goodImages, badImages);
learningAlgo = new SVM_Matlab(userPath, sigma, 1, SVM_Matlab.Kernel.SVM_RBF, goodImages, badImages);
crossValidation(goodImages, badImages);
}
}
}
public void Tests(out ImageVector[] goodImages, out ImageVector[] badImages)
{
goodImages = null;
badImages = null;
// Get list of files in folders
string[] filesAll = Testing.GetFilesFromDirectory(allPath).ToArray();
string[] filesTrue = Testing.GetFilesFromDirectory(truePath).ToArray();
if (filesAll.Length <= 0 || filesTrue.Length <= 0)
return;
string[] filesFalse = DataConverter.Internal_ExtractFalseFiles(filesAll, filesTrue);
//contains the parameters for each picture and the picture path
extractAlbum(out goodImages, out badImages, filesTrue, filesFalse, userPath, false);
// remove comment if you want to optimize one of the algorithms
// optimize svm
// Internal_SVM(goodImages, badImages);
// optimize knn
// Internal_KNN(goodImages, badImages);
// optimize knn and svm`
// Internal_KNN_SVM(goodImages, badImages, true); //regular intersection
//testIntersection(goodImages, badImages); //smart intersection
int k = 2;
for (double i = 0; i <= 5; i += 1)
{
for (double j = 0; j <= 0; j += 1)
{
double sigma = Math.Pow(2, i);
double gamma = Math.Pow(2, j);
learningAlgo = new KNN_SVM(userPath, sigma, gamma, KNN_SVM.Kernel.SVM_RBF, k, goodImages, badImages);
}
}
// test using cross validation
//learningAlgo = new SVM_Matlab(userPath, -5, -5, SVM_Matlab.Kernel.SVM_RBF);
//svm = new SVM(SVM.Kernel.SVM_RBF, 1, 1);
//crossValidation(goodImages, badImages);
// test on different albums
//differentAlbums(goodImages, badImages);
// copyGoodImages(filesTrue, filesFalse);
learningAlgo.Quit();
}
//, Boolean isTest)
/*
* this test is for finding the best optimization for SVM algorithm
* trying sigma and gamma for the rbf kernel for best accuracy.
*/
public void Internal_KNN_SVM(ImageVector[] goodImages, ImageVector[] badImages)
{
int k = 2;
for (double i = 0; i <= 5; i += 1)
{
for (double j = 0; j <= 0; j += 1)
{
double sigma = Math.Pow(2, i);
double gamma = Math.Pow(2, j);
learningAlgo = new KNN_SVM(userPath, sigma, gamma, KNN_SVM.Kernel.SVM_RBF, k, goodImages, badImages);
if (!isTest)
{
crossValidation(goodImages, badImages);
}
else
crossValidation_second(goodImages, badImages);
}
}
}
public void testIntersection(ImageVector[] goodImages, ImageVector[] badImages)
{
for (double conf = 0.5; conf <= 0.5; conf += 0.1)
{
SVM_Matlab.CONFIDENCE_BAR = conf;
learningAlgo = new smartIntersection(userPath, goodImages, badImages);
crossValidation(goodImages, badImages);
}
}
/*
* this test is for finding the best optimization for KNN algorithm
* finding the best k for this classification
*/
public void Internal_KNN(ImageVector[] goodImages, ImageVector[] badImages)
{
for (int k = 2; k <= 2; k++)
{
learningAlgo = new KNN_Matlab(userPath, k, goodImages, badImages);
crossValidation(goodImages, badImages);
}
}
